{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "\n",
    "plt.style.use('ggplot')\n",
    "plt.rcParams['figure.figsize'] = (15, 3)\n",
    "plt.rcParams['font.family'] = 'sans-serif'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Summary"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "By the end of this chapter, we're going to have downloaded all of Canada's weather data for 2012, and saved it to a CSV. \n",
    "\n",
    "We'll do this by downloading it one month at a time, and then combining all the months together.\n",
    "\n",
    "Here's the temperature every hour for 2012!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x114b819a0>"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "weather_2012_final = pd.read_csv('../data/weather_2012.csv', index_col='Date/Time')\n",
    "weather_2012_final['Temp (C)'].plot(figsize=(15, 6))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 5.1 Downloading one month of weather data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "When playing with the cycling data, I wanted temperature and precipitation data to find out if people like biking when it's raining. So I went to the site for [Canadian historical weather data](http://climate.weather.gc.ca/index_e.html#access), and figured out how to get it automatically.\n",
    "\n",
    "Here we're going to get the data for March 2012, and clean it up\n",
    "\n",
    "Here's an URL template you can use to get data in Montreal. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "url_template = \"http://climate.weather.gc.ca/climateData/bulkdata_e.html?format=csv&stationID=5415&Year={year}&Month={month}&timeframe=1&submit=Download+Data\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To get the data for March 2013, we need to format it with `month=3, year=2012`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "#url = url_template.format(month=3, year=2012)\n",
    "#weather_mar2012 = pd.read_csv(url, skiprows=15, index_col='Date/Time', parse_dates=True, encoding='latin1', header=True)\n",
    "\n",
    "# because the url is broken, we use our saved dataframe for now\n",
    "weather_mar2012 = pd.read_csv('../data/weather_2012.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This is super great! We can just use the same `read_csv` function as before, and just give it a URL as a filename. Awesome.\n",
    "\n",
    "There are 16 rows of metadata at the top of this CSV, but pandas knows CSVs are weird, so there's a `skiprows` options. We parse the dates again, and set 'Date/Time' to be the index column. Here's the resulting dataframe."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Date/Time</th>\n",
       "      <th>Temp (C)</th>\n",
       "      <th>Dew Point Temp (C)</th>\n",
       "      <th>Rel Hum (%)</th>\n",
       "      <th>Wind Spd (km/h)</th>\n",
       "      <th>Visibility (km)</th>\n",
       "      <th>Stn Press (kPa)</th>\n",
       "      <th>Weather</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2012-01-01 00:00:00</td>\n",
       "      <td>-1.8</td>\n",
       "      <td>-3.9</td>\n",
       "      <td>86</td>\n",
       "      <td>4</td>\n",
       "      <td>8.0</td>\n",
       "      <td>101.24</td>\n",
       "      <td>Fog</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2012-01-01 01:00:00</td>\n",
       "      <td>-1.8</td>\n",
       "      <td>-3.7</td>\n",
       "      <td>87</td>\n",
       "      <td>4</td>\n",
       "      <td>8.0</td>\n",
       "      <td>101.24</td>\n",
       "      <td>Fog</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2012-01-01 02:00:00</td>\n",
       "      <td>-1.8</td>\n",
       "      <td>-3.4</td>\n",
       "      <td>89</td>\n",
       "      <td>7</td>\n",
       "      <td>4.0</td>\n",
       "      <td>101.26</td>\n",
       "      <td>Freezing Drizzle,Fog</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2012-01-01 03:00:00</td>\n",
       "      <td>-1.5</td>\n",
       "      <td>-3.2</td>\n",
       "      <td>88</td>\n",
       "      <td>6</td>\n",
       "      <td>4.0</td>\n",
       "      <td>101.27</td>\n",
       "      <td>Freezing Drizzle,Fog</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2012-01-01 04:00:00</td>\n",
       "      <td>-1.5</td>\n",
       "      <td>-3.3</td>\n",
       "      <td>88</td>\n",
       "      <td>7</td>\n",
       "      <td>4.8</td>\n",
       "      <td>101.23</td>\n",
       "      <td>Fog</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8779</th>\n",
       "      <td>2012-12-31 19:00:00</td>\n",
       "      <td>0.1</td>\n",
       "      <td>-2.7</td>\n",
       "      <td>81</td>\n",
       "      <td>30</td>\n",
       "      <td>9.7</td>\n",
       "      <td>100.13</td>\n",
       "      <td>Snow</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8780</th>\n",
       "      <td>2012-12-31 20:00:00</td>\n",
       "      <td>0.2</td>\n",
       "      <td>-2.4</td>\n",
       "      <td>83</td>\n",
       "      <td>24</td>\n",
       "      <td>9.7</td>\n",
       "      <td>100.03</td>\n",
       "      <td>Snow</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8781</th>\n",
       "      <td>2012-12-31 21:00:00</td>\n",
       "      <td>-0.5</td>\n",
       "      <td>-1.5</td>\n",
       "      <td>93</td>\n",
       "      <td>28</td>\n",
       "      <td>4.8</td>\n",
       "      <td>99.95</td>\n",
       "      <td>Snow</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8782</th>\n",
       "      <td>2012-12-31 22:00:00</td>\n",
       "      <td>-0.2</td>\n",
       "      <td>-1.8</td>\n",
       "      <td>89</td>\n",
       "      <td>28</td>\n",
       "      <td>9.7</td>\n",
       "      <td>99.91</td>\n",
       "      <td>Snow</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8783</th>\n",
       "      <td>2012-12-31 23:00:00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-2.1</td>\n",
       "      <td>86</td>\n",
       "      <td>30</td>\n",
       "      <td>11.3</td>\n",
       "      <td>99.89</td>\n",
       "      <td>Snow</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8784 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                Date/Time  Temp (C)  Dew Point Temp (C)  Rel Hum (%)  \\\n",
       "0     2012-01-01 00:00:00      -1.8                -3.9           86   \n",
       "1     2012-01-01 01:00:00      -1.8                -3.7           87   \n",
       "2     2012-01-01 02:00:00      -1.8                -3.4           89   \n",
       "3     2012-01-01 03:00:00      -1.5                -3.2           88   \n",
       "4     2012-01-01 04:00:00      -1.5                -3.3           88   \n",
       "...                   ...       ...                 ...          ...   \n",
       "8779  2012-12-31 19:00:00       0.1                -2.7           81   \n",
       "8780  2012-12-31 20:00:00       0.2                -2.4           83   \n",
       "8781  2012-12-31 21:00:00      -0.5                -1.5           93   \n",
       "8782  2012-12-31 22:00:00      -0.2                -1.8           89   \n",
       "8783  2012-12-31 23:00:00       0.0                -2.1           86   \n",
       "\n",
       "      Wind Spd (km/h)  Visibility (km)  Stn Press (kPa)               Weather  \n",
       "0                   4              8.0           101.24                   Fog  \n",
       "1                   4              8.0           101.24                   Fog  \n",
       "2                   7              4.0           101.26  Freezing Drizzle,Fog  \n",
       "3                   6              4.0           101.27  Freezing Drizzle,Fog  \n",
       "4                   7              4.8           101.23                   Fog  \n",
       "...               ...              ...              ...                   ...  \n",
       "8779               30              9.7           100.13                  Snow  \n",
       "8780               24              9.7           100.03                  Snow  \n",
       "8781               28              4.8            99.95                  Snow  \n",
       "8782               28              9.7            99.91                  Snow  \n",
       "8783               30             11.3            99.89                  Snow  \n",
       "\n",
       "[8784 rows x 8 columns]"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "weather_mar2012"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's plot it!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1150d5250>"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "weather_mar2012[u\"Temp (C)\"].plot(figsize=(15, 5))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Notice how it goes up to 25° C in the middle there? That was a big deal. It was March, and people were wearing shorts outside. \n",
    "\n",
    "And I was out of town and I missed it. Still sad, humans.\n",
    "\n",
    "I had to write `'\\xb0'` for that degree character °. Let's fix up the columns. We're going to just print them out, copy, and fix them up by hand."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "Length mismatch: Expected axis has 8 elements, new values have 24 elements",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-24-e81e0193d388>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m weather_mar2012.columns = [\n\u001b[0m\u001b[1;32m      2\u001b[0m     \u001b[0;34mu'Year'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34mu'Month'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34mu'Day'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34mu'Time'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34mu'Data Quality'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34mu'Temp (C)'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m     \u001b[0;34mu'Temp Flag'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34mu'Dew Point Temp (C)'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34mu'Dew Point Temp Flag'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m     \u001b[0;34mu'Rel Hum (%)'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34mu'Rel Hum Flag'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34mu'Wind Dir (10s deg)'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34mu'Wind Dir Flag'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m     \u001b[0;34mu'Wind Spd (km/h)'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34mu'Wind Spd Flag'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34mu'Visibility (km)'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34mu'Visibility Flag'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/.virtualenvs/cookbook/lib/python3.8/site-packages/pandas/core/generic.py\u001b[0m in \u001b[0;36m__setattr__\u001b[0;34m(self, name, value)\u001b[0m\n\u001b[1;32m   5141\u001b[0m         \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   5142\u001b[0m             \u001b[0mobject\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__getattribute__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mname\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 5143\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mobject\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__setattr__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mname\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvalue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   5144\u001b[0m         \u001b[0;32mexcept\u001b[0m \u001b[0mAttributeError\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   5145\u001b[0m             \u001b[0;32mpass\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32mpandas/_libs/properties.pyx\u001b[0m in \u001b[0;36mpandas._libs.properties.AxisProperty.__set__\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;32m~/.virtualenvs/cookbook/lib/python3.8/site-packages/pandas/core/generic.py\u001b[0m in \u001b[0;36m_set_axis\u001b[0;34m(self, axis, labels)\u001b[0m\n\u001b[1;32m    562\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_set_axis\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mint\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlabels\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mIndex\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m->\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    563\u001b[0m         \u001b[0mlabels\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mensure_index\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlabels\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 564\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_mgr\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mset_axis\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0maxis\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlabels\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    565\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_clear_item_cache\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    566\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/.virtualenvs/cookbook/lib/python3.8/site-packages/pandas/core/internals/managers.py\u001b[0m in \u001b[0;36mset_axis\u001b[0;34m(self, axis, new_labels)\u001b[0m\n\u001b[1;32m    214\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    215\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mnew_len\u001b[0m \u001b[0;34m!=\u001b[0m \u001b[0mold_len\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 216\u001b[0;31m             raise ValueError(\n\u001b[0m\u001b[1;32m    217\u001b[0m                 \u001b[0;34mf\"Length mismatch: Expected axis has {old_len} elements, new \"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    218\u001b[0m                 \u001b[0;34mf\"values have {new_len} elements\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mValueError\u001b[0m: Length mismatch: Expected axis has 8 elements, new values have 24 elements"
     ]
    }
   ],
   "source": [
    "weather_mar2012.columns = [\n",
    "    u'Year', u'Month', u'Day', u'Time', u'Data Quality', u'Temp (C)', \n",
    "    u'Temp Flag', u'Dew Point Temp (C)', u'Dew Point Temp Flag', \n",
    "    u'Rel Hum (%)', u'Rel Hum Flag', u'Wind Dir (10s deg)', u'Wind Dir Flag', \n",
    "    u'Wind Spd (km/h)', u'Wind Spd Flag', u'Visibility (km)', u'Visibility Flag',\n",
    "    u'Stn Press (kPa)', u'Stn Press Flag', u'Hmdx', u'Hmdx Flag', u'Wind Chill', \n",
    "    u'Wind Chill Flag', u'Weather']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You'll notice in the summary above that there are a few columns which are are either entirely empty or only have a few values in them. Let's get rid of all of those with `dropna`.\n",
    "\n",
    "The argument `axis=1` to `dropna` means \"drop columns\", not rows\", and `how='any'` means \"drop the column if any value is null\". \n",
    "\n",
    "This is much better now -- we only have columns with real data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Year</th>\n",
       "      <th>Month</th>\n",
       "      <th>Day</th>\n",
       "      <th>Time</th>\n",
       "      <th>Data Quality</th>\n",
       "      <th>Temp (C)</th>\n",
       "      <th>Dew Point Temp (C)</th>\n",
       "      <th>Rel Hum (%)</th>\n",
       "      <th>Wind Spd (km/h)</th>\n",
       "      <th>Visibility (km)</th>\n",
       "      <th>Stn Press (kPa)</th>\n",
       "      <th>Weather</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date/Time</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2012-03-01 00:00:00</th>\n",
       "      <td> 2012</td>\n",
       "      <td> 3</td>\n",
       "      <td> 1</td>\n",
       "      <td> 00:00</td>\n",
       "      <td>  </td>\n",
       "      <td>-5.5</td>\n",
       "      <td>-9.7</td>\n",
       "      <td> 72</td>\n",
       "      <td> 24</td>\n",
       "      <td> 4.0</td>\n",
       "      <td> 100.97</td>\n",
       "      <td> Snow</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-03-01 01:00:00</th>\n",
       "      <td> 2012</td>\n",
       "      <td> 3</td>\n",
       "      <td> 1</td>\n",
       "      <td> 01:00</td>\n",
       "      <td>  </td>\n",
       "      <td>-5.7</td>\n",
       "      <td>-8.7</td>\n",
       "      <td> 79</td>\n",
       "      <td> 26</td>\n",
       "      <td> 2.4</td>\n",
       "      <td> 100.87</td>\n",
       "      <td> Snow</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-03-01 02:00:00</th>\n",
       "      <td> 2012</td>\n",
       "      <td> 3</td>\n",
       "      <td> 1</td>\n",
       "      <td> 02:00</td>\n",
       "      <td>  </td>\n",
       "      <td>-5.4</td>\n",
       "      <td>-8.3</td>\n",
       "      <td> 80</td>\n",
       "      <td> 28</td>\n",
       "      <td> 4.8</td>\n",
       "      <td> 100.80</td>\n",
       "      <td> Snow</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-03-01 03:00:00</th>\n",
       "      <td> 2012</td>\n",
       "      <td> 3</td>\n",
       "      <td> 1</td>\n",
       "      <td> 03:00</td>\n",
       "      <td>  </td>\n",
       "      <td>-4.7</td>\n",
       "      <td>-7.7</td>\n",
       "      <td> 79</td>\n",
       "      <td> 28</td>\n",
       "      <td> 4.0</td>\n",
       "      <td> 100.69</td>\n",
       "      <td> Snow</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-03-01 04:00:00</th>\n",
       "      <td> 2012</td>\n",
       "      <td> 3</td>\n",
       "      <td> 1</td>\n",
       "      <td> 04:00</td>\n",
       "      <td>  </td>\n",
       "      <td>-5.4</td>\n",
       "      <td>-7.8</td>\n",
       "      <td> 83</td>\n",
       "      <td> 35</td>\n",
       "      <td> 1.6</td>\n",
       "      <td> 100.62</td>\n",
       "      <td> Snow</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     Year  Month  Day   Time Data Quality  Temp (C)  \\\n",
       "Date/Time                                                             \n",
       "2012-03-01 00:00:00  2012      3    1  00:00                   -5.5   \n",
       "2012-03-01 01:00:00  2012      3    1  01:00                   -5.7   \n",
       "2012-03-01 02:00:00  2012      3    1  02:00                   -5.4   \n",
       "2012-03-01 03:00:00  2012      3    1  03:00                   -4.7   \n",
       "2012-03-01 04:00:00  2012      3    1  04:00                   -5.4   \n",
       "\n",
       "                     Dew Point Temp (C)  Rel Hum (%)  Wind Spd (km/h)  \\\n",
       "Date/Time                                                               \n",
       "2012-03-01 00:00:00                -9.7           72               24   \n",
       "2012-03-01 01:00:00                -8.7           79               26   \n",
       "2012-03-01 02:00:00                -8.3           80               28   \n",
       "2012-03-01 03:00:00                -7.7           79               28   \n",
       "2012-03-01 04:00:00                -7.8           83               35   \n",
       "\n",
       "                     Visibility (km)  Stn Press (kPa) Weather  \n",
       "Date/Time                                                      \n",
       "2012-03-01 00:00:00              4.0           100.97    Snow  \n",
       "2012-03-01 01:00:00              2.4           100.87    Snow  \n",
       "2012-03-01 02:00:00              4.8           100.80    Snow  \n",
       "2012-03-01 03:00:00              4.0           100.69    Snow  \n",
       "2012-03-01 04:00:00              1.6           100.62    Snow  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "weather_mar2012 = weather_mar2012.dropna(axis=1, how='any')\n",
    "weather_mar2012[:5]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The Year/Month/Day/Time columns are redundant, though, and the Data Quality column doesn't look too useful. Let's get rid of those.\n",
    "\n",
    "The `axis=1` argument means \"Drop columns\", like before. The default for operations like `dropna` and `drop` is always to operate on rows."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Temp (C)</th>\n",
       "      <th>Dew Point Temp (C)</th>\n",
       "      <th>Rel Hum (%)</th>\n",
       "      <th>Wind Spd (km/h)</th>\n",
       "      <th>Visibility (km)</th>\n",
       "      <th>Stn Press (kPa)</th>\n",
       "      <th>Weather</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date/Time</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2012-03-01 00:00:00</th>\n",
       "      <td>-5.5</td>\n",
       "      <td>-9.7</td>\n",
       "      <td> 72</td>\n",
       "      <td> 24</td>\n",
       "      <td> 4.0</td>\n",
       "      <td> 100.97</td>\n",
       "      <td> Snow</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-03-01 01:00:00</th>\n",
       "      <td>-5.7</td>\n",
       "      <td>-8.7</td>\n",
       "      <td> 79</td>\n",
       "      <td> 26</td>\n",
       "      <td> 2.4</td>\n",
       "      <td> 100.87</td>\n",
       "      <td> Snow</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-03-01 02:00:00</th>\n",
       "      <td>-5.4</td>\n",
       "      <td>-8.3</td>\n",
       "      <td> 80</td>\n",
       "      <td> 28</td>\n",
       "      <td> 4.8</td>\n",
       "      <td> 100.80</td>\n",
       "      <td> Snow</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-03-01 03:00:00</th>\n",
       "      <td>-4.7</td>\n",
       "      <td>-7.7</td>\n",
       "      <td> 79</td>\n",
       "      <td> 28</td>\n",
       "      <td> 4.0</td>\n",
       "      <td> 100.69</td>\n",
       "      <td> Snow</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-03-01 04:00:00</th>\n",
       "      <td>-5.4</td>\n",
       "      <td>-7.8</td>\n",
       "      <td> 83</td>\n",
       "      <td> 35</td>\n",
       "      <td> 1.6</td>\n",
       "      <td> 100.62</td>\n",
       "      <td> Snow</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     Temp (C)  Dew Point Temp (C)  Rel Hum (%)  \\\n",
       "Date/Time                                                        \n",
       "2012-03-01 00:00:00      -5.5                -9.7           72   \n",
       "2012-03-01 01:00:00      -5.7                -8.7           79   \n",
       "2012-03-01 02:00:00      -5.4                -8.3           80   \n",
       "2012-03-01 03:00:00      -4.7                -7.7           79   \n",
       "2012-03-01 04:00:00      -5.4                -7.8           83   \n",
       "\n",
       "                     Wind Spd (km/h)  Visibility (km)  Stn Press (kPa) Weather  \n",
       "Date/Time                                                                       \n",
       "2012-03-01 00:00:00               24              4.0           100.97    Snow  \n",
       "2012-03-01 01:00:00               26              2.4           100.87    Snow  \n",
       "2012-03-01 02:00:00               28              4.8           100.80    Snow  \n",
       "2012-03-01 03:00:00               28              4.0           100.69    Snow  \n",
       "2012-03-01 04:00:00               35              1.6           100.62    Snow  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "weather_mar2012 = weather_mar2012.drop(['Year', 'Month', 'Day', 'Time', 'Data Quality'], axis=1)\n",
    "weather_mar2012[:5]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Awesome! We now only have the relevant columns, and it's much more manageable."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 5.2 Plotting the temperature by hour of day"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This one's just for fun -- we've already done this before, using groupby and aggregate! We will learn whether or not it gets colder at night. Well, obviously. But let's do it anyway."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<bound method DataFrame.head of                      Temp (C)\n",
      "Date/Time                    \n",
      "2012-03-01 00:00:00      -5.5\n",
      "2012-03-01 01:00:00      -5.7\n",
      "2012-03-01 02:00:00      -5.4\n",
      "2012-03-01 03:00:00      -4.7\n",
      "2012-03-01 04:00:00      -5.4\n",
      "2012-03-01 05:00:00      -5.3\n",
      "2012-03-01 06:00:00      -5.2\n",
      "2012-03-01 07:00:00      -4.9\n",
      "2012-03-01 08:00:00      -5.0\n",
      "2012-03-01 09:00:00      -4.9\n",
      "2012-03-01 10:00:00      -4.7\n",
      "2012-03-01 11:00:00      -4.4\n",
      "2012-03-01 12:00:00      -4.3\n",
      "2012-03-01 13:00:00      -4.3\n",
      "2012-03-01 14:00:00      -3.9\n",
      "2012-03-01 15:00:00      -3.3\n",
      "2012-03-01 16:00:00      -2.7\n",
      "2012-03-01 17:00:00      -2.9\n",
      "2012-03-01 18:00:00      -3.0\n",
      "2012-03-01 19:00:00      -3.6\n",
      "2012-03-01 20:00:00      -3.7\n",
      "2012-03-01 21:00:00      -3.9\n",
      "2012-03-01 22:00:00      -4.3\n",
      "2012-03-01 23:00:00      -4.3\n",
      "2012-03-02 00:00:00      -4.8\n",
      "2012-03-02 01:00:00      -5.3\n",
      "2012-03-02 02:00:00      -5.2\n",
      "2012-03-02 03:00:00      -5.5\n",
      "2012-03-02 04:00:00      -5.6\n",
      "2012-03-02 05:00:00      -5.5\n",
      "...                       ...\n",
      "2012-03-30 18:00:00       3.9\n",
      "2012-03-30 19:00:00       3.1\n",
      "2012-03-30 20:00:00       3.0\n",
      "2012-03-30 21:00:00       1.7\n",
      "2012-03-30 22:00:00       0.4\n",
      "2012-03-30 23:00:00       1.4\n",
      "2012-03-31 00:00:00       1.5\n",
      "2012-03-31 01:00:00       1.3\n",
      "2012-03-31 02:00:00       1.3\n",
      "2012-03-31 03:00:00       0.7\n",
      "2012-03-31 04:00:00      -0.9\n",
      "2012-03-31 05:00:00      -0.6\n",
      "2012-03-31 06:00:00      -0.5\n",
      "2012-03-31 07:00:00      -0.3\n",
      "2012-03-31 08:00:00       0.7\n",
      "2012-03-31 09:00:00       1.5\n",
      "2012-03-31 10:00:00       2.9\n",
      "2012-03-31 11:00:00       4.6\n",
      "2012-03-31 12:00:00       6.4\n",
      "2012-03-31 13:00:00       6.5\n",
      "2012-03-31 14:00:00       7.7\n",
      "2012-03-31 15:00:00       7.7\n",
      "2012-03-31 16:00:00       8.4\n",
      "2012-03-31 17:00:00       7.9\n",
      "2012-03-31 18:00:00       7.0\n",
      "2012-03-31 19:00:00       5.9\n",
      "2012-03-31 20:00:00       4.4\n",
      "2012-03-31 21:00:00       2.6\n",
      "2012-03-31 22:00:00       2.7\n",
      "2012-03-31 23:00:00       1.5\n",
      "\n",
      "[744 rows x 1 columns]>\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fbac6876b10>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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Wc8zWrVsHhUKBa6655lv3cY0ZEZH41Ds9+MeOFuxp7cXSKclYOCERXHhORBSd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\nRBHGxoyIiIiIiCjC2JgRERERERFF2P8HnahJd/NvNvMAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fbac67dfa90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "temperatures = weather_mar2012[[u'Temp (C)']].copy()\n",
    "print(temperatures.head)\n",
    "temperatures.loc[:,'Hour'] = weather_mar2012.index.hour\n",
    "temperatures.groupby('Hour').aggregate(np.median).plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "So it looks like the time with the highest median temperature is 2pm. Neat."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 5.3 Getting the whole year of data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Okay, so what if we want the data for the whole year? Ideally the API would just let us download that, but I couldn't figure out a way to do that.\n",
    "\n",
    "First, let's put our work from above into a function that gets the weather for a given month. \n",
    "\n",
    "I noticed that there's an irritating bug where when I ask for January, it gives me data for the previous year, so we'll fix that too. [no, really. You can check =)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "def download_weather_month(year, month):\n",
    "    if month == 1:\n",
    "        year += 1\n",
    "    url = url_template.format(year=year, month=month)\n",
    "    weather_data = pd.read_csv(url, skiprows=15, index_col='Date/Time', parse_dates=True, header=True)\n",
    "    weather_data = weather_data.dropna(axis=1)\n",
    "    weather_data.columns = [col.replace('\\xb0', '') for col in weather_data.columns]\n",
    "    weather_data = weather_data.drop(['Year', 'Day', 'Month', 'Time', 'Data Quality'], axis=1)\n",
    "    return weather_data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can test that this function does the right thing:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Temp (C)</th>\n",
       "      <th>Dew Point Temp (C)</th>\n",
       "      <th>Rel Hum (%)</th>\n",
       "      <th>Wind Spd (km/h)</th>\n",
       "      <th>Visibility (km)</th>\n",
       "      <th>Stn Press (kPa)</th>\n",
       "      <th>Weather</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date/Time</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2013-01-01 00:00:00</th>\n",
       "      <td>-1.0</td>\n",
       "      <td> -1.7</td>\n",
       "      <td> 95</td>\n",
       "      <td> 35</td>\n",
       "      <td>  6.4</td>\n",
       "      <td>  99.89</td>\n",
       "      <td>         Snow</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-01 01:00:00</th>\n",
       "      <td>-2.0</td>\n",
       "      <td> -5.1</td>\n",
       "      <td> 79</td>\n",
       "      <td> 35</td>\n",
       "      <td> 16.1</td>\n",
       "      <td>  99.93</td>\n",
       "      <td> Mainly Clear</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-01 02:00:00</th>\n",
       "      <td>-2.7</td>\n",
       "      <td> -6.0</td>\n",
       "      <td> 78</td>\n",
       "      <td> 28</td>\n",
       "      <td> 19.3</td>\n",
       "      <td> 100.08</td>\n",
       "      <td>         Snow</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-01 03:00:00</th>\n",
       "      <td>-5.6</td>\n",
       "      <td>-11.7</td>\n",
       "      <td> 62</td>\n",
       "      <td> 30</td>\n",
       "      <td> 25.0</td>\n",
       "      <td> 100.21</td>\n",
       "      <td>        Clear</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-01 04:00:00</th>\n",
       "      <td>-7.7</td>\n",
       "      <td>-12.6</td>\n",
       "      <td> 68</td>\n",
       "      <td> 35</td>\n",
       "      <td> 19.3</td>\n",
       "      <td> 100.32</td>\n",
       "      <td> Mainly Clear</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     Temp (C)  Dew Point Temp (C)  Rel Hum (%)  \\\n",
       "Date/Time                                                        \n",
       "2013-01-01 00:00:00      -1.0                -1.7           95   \n",
       "2013-01-01 01:00:00      -2.0                -5.1           79   \n",
       "2013-01-01 02:00:00      -2.7                -6.0           78   \n",
       "2013-01-01 03:00:00      -5.6               -11.7           62   \n",
       "2013-01-01 04:00:00      -7.7               -12.6           68   \n",
       "\n",
       "                     Wind Spd (km/h)  Visibility (km)  Stn Press (kPa)  \\\n",
       "Date/Time                                                                \n",
       "2013-01-01 00:00:00               35              6.4            99.89   \n",
       "2013-01-01 01:00:00               35             16.1            99.93   \n",
       "2013-01-01 02:00:00               28             19.3           100.08   \n",
       "2013-01-01 03:00:00               30             25.0           100.21   \n",
       "2013-01-01 04:00:00               35             19.3           100.32   \n",
       "\n",
       "                          Weather  \n",
       "Date/Time                          \n",
       "2013-01-01 00:00:00          Snow  \n",
       "2013-01-01 01:00:00  Mainly Clear  \n",
       "2013-01-01 02:00:00          Snow  \n",
       "2013-01-01 03:00:00         Clear  \n",
       "2013-01-01 04:00:00  Mainly Clear  "
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "download_weather_month(2012, 1)[:5]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we can get all the months at once. This will take a little while to run."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_by_month = [download_weather_month(2012, i) for i in range(1, 13)]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Once we have this, it's easy to concatenate all the dataframes together into one big dataframe using `pd.concat`. And now we have the whole year's data!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Temp (C)</th>\n",
       "      <th>Dew Point Temp (C)</th>\n",
       "      <th>Rel Hum (%)</th>\n",
       "      <th>Wind Spd (km/h)</th>\n",
       "      <th>Visibility (km)</th>\n",
       "      <th>Stn Press (kPa)</th>\n",
       "      <th>Weather</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date/Time</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2013-01-01 00:00:00</th>\n",
       "      <td> -1.0</td>\n",
       "      <td> -1.7</td>\n",
       "      <td> 95</td>\n",
       "      <td> 35</td>\n",
       "      <td>  6.4</td>\n",
       "      <td>  99.89</td>\n",
       "      <td>          Snow</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-01 01:00:00</th>\n",
       "      <td> -2.0</td>\n",
       "      <td> -5.1</td>\n",
       "      <td> 79</td>\n",
       "      <td> 35</td>\n",
       "      <td> 16.1</td>\n",
       "      <td>  99.93</td>\n",
       "      <td>  Mainly Clear</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-01 02:00:00</th>\n",
       "      <td> -2.7</td>\n",
       "      <td> -6.0</td>\n",
       "      <td> 78</td>\n",
       "      <td> 28</td>\n",
       "      <td> 19.3</td>\n",
       "      <td> 100.08</td>\n",
       "      <td>          Snow</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-01 03:00:00</th>\n",
       "      <td> -5.6</td>\n",
       "      <td>-11.7</td>\n",
       "      <td> 62</td>\n",
       "      <td> 30</td>\n",
       "      <td> 25.0</td>\n",
       "      <td> 100.21</td>\n",
       "      <td>         Clear</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-01 04:00:00</th>\n",
       "      <td> -7.7</td>\n",
       "      <td>-12.6</td>\n",
       "      <td> 68</td>\n",
       "      <td> 35</td>\n",
       "      <td> 19.3</td>\n",
       "      <td> 100.32</td>\n",
       "      <td>  Mainly Clear</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-01 05:00:00</th>\n",
       "      <td> -9.7</td>\n",
       "      <td>-14.8</td>\n",
       "      <td> 66</td>\n",
       "      <td> 33</td>\n",
       "      <td> 25.0</td>\n",
       "      <td> 100.47</td>\n",
       "      <td>         Clear</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-01 06:00:00</th>\n",
       "      <td>-11.1</td>\n",
       "      <td>-17.0</td>\n",
       "      <td> 62</td>\n",
       "      <td> 30</td>\n",
       "      <td> 25.0</td>\n",
       "      <td> 100.65</td>\n",
       "      <td>         Clear</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-01 07:00:00</th>\n",
       "      <td>-12.2</td>\n",
       "      <td>-17.2</td>\n",
       "      <td> 66</td>\n",
       "      <td> 20</td>\n",
       "      <td> 25.0</td>\n",
       "      <td> 100.78</td>\n",
       "      <td>         Clear</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-01 08:00:00</th>\n",
       "      <td>-13.0</td>\n",
       "      <td>-17.7</td>\n",
       "      <td> 68</td>\n",
       "      <td> 13</td>\n",
       "      <td> 24.1</td>\n",
       "      <td> 100.87</td>\n",
       "      <td>         Clear</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-01 09:00:00</th>\n",
       "      <td>-13.0</td>\n",
       "      <td>-17.3</td>\n",
       "      <td> 70</td>\n",
       "      <td> 20</td>\n",
       "      <td> 24.1</td>\n",
       "      <td> 100.86</td>\n",
       "      <td>         Clear</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-01 10:00:00</th>\n",
       "      <td>-12.6</td>\n",
       "      <td>-17.8</td>\n",
       "      <td> 65</td>\n",
       "      <td> 19</td>\n",
       "      <td> 24.1</td>\n",
       "      <td> 100.90</td>\n",
       "      <td>         Clear</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-01 11:00:00</th>\n",
       "      <td>-12.2</td>\n",
       "      <td>-17.6</td>\n",
       "      <td> 64</td>\n",
       "      <td> 22</td>\n",
       "      <td> 24.1</td>\n",
       "      <td> 100.88</td>\n",
       "      <td>  Mainly Clear</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-01 12:00:00</th>\n",
       "      <td>-11.8</td>\n",
       "      <td>-17.2</td>\n",
       "      <td> 64</td>\n",
       "      <td> 26</td>\n",
       "      <td> 24.1</td>\n",
       "      <td> 100.87</td>\n",
       "      <td>  Mainly Clear</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-01 13:00:00</th>\n",
       "      <td>-11.3</td>\n",
       "      <td>-17.4</td>\n",
       "      <td> 61</td>\n",
       "      <td> 26</td>\n",
       "      <td> 24.1</td>\n",
       "      <td> 100.83</td>\n",
       "      <td>  Mainly Clear</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-01 14:00:00</th>\n",
       "      <td>-11.3</td>\n",
       "      <td>-17.4</td>\n",
       "      <td> 61</td>\n",
       "      <td> 28</td>\n",
       "      <td> 24.1</td>\n",
       "      <td> 100.82</td>\n",
       "      <td>  Mainly Clear</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-01 15:00:00</th>\n",
       "      <td>-11.4</td>\n",
       "      <td>-17.6</td>\n",
       "      <td> 60</td>\n",
       "      <td> 30</td>\n",
       "      <td> 24.1</td>\n",
       "      <td> 100.85</td>\n",
       "      <td>  Mainly Clear</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-01 16:00:00</th>\n",
       "      <td>-12.0</td>\n",
       "      <td>-18.0</td>\n",
       "      <td> 61</td>\n",
       "      <td> 22</td>\n",
       "      <td> 24.1</td>\n",
       "      <td> 100.81</td>\n",
       "      <td>  Mainly Clear</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-01 17:00:00</th>\n",
       "      <td>-13.0</td>\n",
       "      <td>-18.4</td>\n",
       "      <td> 64</td>\n",
       "      <td> 19</td>\n",
       "      <td> 25.0</td>\n",
       "      <td> 100.90</td>\n",
       "      <td>         Clear</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-01 18:00:00</th>\n",
       "      <td>-13.4</td>\n",
       "      <td>-18.4</td>\n",
       "      <td> 66</td>\n",
       "      <td> 24</td>\n",
       "      <td> 25.0</td>\n",
       "      <td> 100.96</td>\n",
       "      <td>         Clear</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-01 19:00:00</th>\n",
       "      <td>-14.1</td>\n",
       "      <td>-18.7</td>\n",
       "      <td> 68</td>\n",
       "      <td> 20</td>\n",
       "      <td> 25.0</td>\n",
       "      <td> 101.02</td>\n",
       "      <td>         Clear</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-01 20:00:00</th>\n",
       "      <td>-14.3</td>\n",
       "      <td>-19.0</td>\n",
       "      <td> 67</td>\n",
       "      <td> 15</td>\n",
       "      <td> 25.0</td>\n",
       "      <td> 101.04</td>\n",
       "      <td>         Clear</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-01 21:00:00</th>\n",
       "      <td>-14.8</td>\n",
       "      <td>-19.5</td>\n",
       "      <td> 67</td>\n",
       "      <td> 15</td>\n",
       "      <td> 25.0</td>\n",
       "      <td> 100.98</td>\n",
       "      <td>  Mainly Clear</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-01 22:00:00</th>\n",
       "      <td>-16.3</td>\n",
       "      <td>-20.2</td>\n",
       "      <td> 72</td>\n",
       "      <td>  7</td>\n",
       "      <td> 25.0</td>\n",
       "      <td> 100.98</td>\n",
       "      <td> Mostly Cloudy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-01 23:00:00</th>\n",
       "      <td>-15.4</td>\n",
       "      <td>-19.8</td>\n",
       "      <td> 69</td>\n",
       "      <td> 11</td>\n",
       "      <td> 25.0</td>\n",
       "      <td> 100.99</td>\n",
       "      <td>        Cloudy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-02 00:00:00</th>\n",
       "      <td>-14.0</td>\n",
       "      <td>-18.4</td>\n",
       "      <td> 69</td>\n",
       "      <td> 11</td>\n",
       "      <td> 19.3</td>\n",
       "      <td> 100.96</td>\n",
       "      <td>          Snow</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-02 01:00:00</th>\n",
       "      <td>-14.1</td>\n",
       "      <td>-18.3</td>\n",
       "      <td> 70</td>\n",
       "      <td> 11</td>\n",
       "      <td> 25.0</td>\n",
       "      <td> 100.91</td>\n",
       "      <td> Mostly Cloudy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-02 02:00:00</th>\n",
       "      <td>-14.3</td>\n",
       "      <td>-18.3</td>\n",
       "      <td> 72</td>\n",
       "      <td> 13</td>\n",
       "      <td> 25.0</td>\n",
       "      <td> 100.94</td>\n",
       "      <td>  Snow Showers</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-02 03:00:00</th>\n",
       "      <td>-14.7</td>\n",
       "      <td>-18.0</td>\n",
       "      <td> 76</td>\n",
       "      <td>  9</td>\n",
       "      <td> 19.3</td>\n",
       "      <td> 100.91</td>\n",
       "      <td>          Snow</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-02 04:00:00</th>\n",
       "      <td>-14.2</td>\n",
       "      <td>-17.1</td>\n",
       "      <td> 79</td>\n",
       "      <td>  6</td>\n",
       "      <td>  9.7</td>\n",
       "      <td> 100.83</td>\n",
       "      <td>          Snow</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-02 05:00:00</th>\n",
       "      <td>-14.3</td>\n",
       "      <td>-17.0</td>\n",
       "      <td> 80</td>\n",
       "      <td>  0</td>\n",
       "      <td>  6.4</td>\n",
       "      <td> 100.81</td>\n",
       "      <td>          Snow</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-12-30 18:00:00</th>\n",
       "      <td>-12.6</td>\n",
       "      <td>-16.0</td>\n",
       "      <td> 76</td>\n",
       "      <td> 24</td>\n",
       "      <td> 25.0</td>\n",
       "      <td> 101.36</td>\n",
       "      <td>  Mainly Clear</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-12-30 19:00:00</th>\n",
       "      <td>-13.4</td>\n",
       "      <td>-16.5</td>\n",
       "      <td> 77</td>\n",
       "      <td> 26</td>\n",
       "      <td> 25.0</td>\n",
       "      <td> 101.47</td>\n",
       "      <td>  Mainly Clear</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-12-30 20:00:00</th>\n",
       "      <td>-13.8</td>\n",
       "      <td>-16.5</td>\n",
       "      <td> 80</td>\n",
       "      <td> 24</td>\n",
       "      <td> 25.0</td>\n",
       "      <td> 101.52</td>\n",
       "      <td>         Clear</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-12-30 21:00:00</th>\n",
       "      <td>-13.8</td>\n",
       "      <td>-16.5</td>\n",
       "      <td> 80</td>\n",
       "      <td> 20</td>\n",
       "      <td> 25.0</td>\n",
       "      <td> 101.50</td>\n",
       "      <td>  Mainly Clear</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-12-30 22:00:00</th>\n",
       "      <td>-13.7</td>\n",
       "      <td>-16.3</td>\n",
       "      <td> 81</td>\n",
       "      <td> 19</td>\n",
       "      <td> 25.0</td>\n",
       "      <td> 101.54</td>\n",
       "      <td>  Mainly Clear</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-12-30 23:00:00</th>\n",
       "      <td>-12.1</td>\n",
       "      <td>-15.1</td>\n",
       "      <td> 78</td>\n",
       "      <td> 28</td>\n",
       "      <td> 25.0</td>\n",
       "      <td> 101.52</td>\n",
       "      <td> Mostly Cloudy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-12-31 00:00:00</th>\n",
       "      <td>-11.1</td>\n",
       "      <td>-14.4</td>\n",
       "      <td> 77</td>\n",
       "      <td> 26</td>\n",
       "      <td> 25.0</td>\n",
       "      <td> 101.51</td>\n",
       "      <td>        Cloudy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-12-31 01:00:00</th>\n",
       "      <td>-10.7</td>\n",
       "      <td>-14.0</td>\n",
       "      <td> 77</td>\n",
       "      <td> 15</td>\n",
       "      <td> 25.0</td>\n",
       "      <td> 101.50</td>\n",
       "      <td>        Cloudy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-12-31 02:00:00</th>\n",
       "      <td>-10.1</td>\n",
       "      <td>-13.4</td>\n",
       "      <td> 77</td>\n",
       "      <td>  9</td>\n",
       "      <td> 25.0</td>\n",
       "      <td> 101.45</td>\n",
       "      <td>        Cloudy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-12-31 03:00:00</th>\n",
       "      <td>-11.8</td>\n",
       "      <td>-14.4</td>\n",
       "      <td> 81</td>\n",
       "      <td>  6</td>\n",
       "      <td> 25.0</td>\n",
       "      <td> 101.42</td>\n",
       "      <td> Mostly Cloudy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-12-31 04:00:00</th>\n",
       "      <td>-10.5</td>\n",
       "      <td>-12.8</td>\n",
       "      <td> 83</td>\n",
       "      <td> 11</td>\n",
       "      <td> 25.0</td>\n",
       "      <td> 101.34</td>\n",
       "      <td>        Cloudy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-12-31 05:00:00</th>\n",
       "      <td>-10.2</td>\n",
       "      <td>-12.4</td>\n",
       "      <td> 84</td>\n",
       "      <td>  6</td>\n",
       "      <td> 25.0</td>\n",
       "      <td> 101.28</td>\n",
       "      <td>        Cloudy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-12-31 06:00:00</th>\n",
       "      <td> -9.7</td>\n",
       "      <td>-11.7</td>\n",
       "      <td> 85</td>\n",
       "      <td>  4</td>\n",
       "      <td> 25.0</td>\n",
       "      <td> 101.23</td>\n",
       "      <td>        Cloudy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-12-31 07:00:00</th>\n",
       "      <td> -9.3</td>\n",
       "      <td>-11.3</td>\n",
       "      <td> 85</td>\n",
       "      <td>  0</td>\n",
       "      <td> 19.3</td>\n",
       "      <td> 101.19</td>\n",
       "      <td>  Snow Showers</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-12-31 08:00:00</th>\n",
       "      <td> -8.6</td>\n",
       "      <td>-10.3</td>\n",
       "      <td> 87</td>\n",
       "      <td>  4</td>\n",
       "      <td>  3.2</td>\n",
       "      <td> 101.14</td>\n",
       "      <td>  Snow Showers</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-12-31 09:00:00</th>\n",
       "      <td> -8.1</td>\n",
       "      <td> -9.6</td>\n",
       "      <td> 89</td>\n",
       "      <td>  4</td>\n",
       "      <td>  2.4</td>\n",
       "      <td> 101.09</td>\n",
       "      <td>          Snow</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-12-31 10:00:00</th>\n",
       "      <td> -7.4</td>\n",
       "      <td> -8.9</td>\n",
       "      <td> 89</td>\n",
       "      <td>  4</td>\n",
       "      <td>  6.4</td>\n",
       "      <td> 101.05</td>\n",
       "      <td>      Snow,Fog</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-12-31 11:00:00</th>\n",
       "      <td> -6.7</td>\n",
       "      <td> -7.9</td>\n",
       "      <td> 91</td>\n",
       "      <td>  9</td>\n",
       "      <td>  9.7</td>\n",
       "      <td> 100.93</td>\n",
       "      <td>          Snow</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-12-31 12:00:00</th>\n",
       "      <td> -5.8</td>\n",
       "      <td> -7.5</td>\n",
       "      <td> 88</td>\n",
       "      <td>  4</td>\n",
       "      <td> 12.9</td>\n",
       "      <td> 100.78</td>\n",
       "      <td>          Snow</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-12-31 13:00:00</th>\n",
       "      <td> -4.6</td>\n",
       "      <td> -6.6</td>\n",
       "      <td> 86</td>\n",
       "      <td>  4</td>\n",
       "      <td> 12.9</td>\n",
       "      <td> 100.63</td>\n",
       "      <td>          Snow</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-12-31 14:00:00</th>\n",
       "      <td> -3.4</td>\n",
       "      <td> -5.7</td>\n",
       "      <td> 84</td>\n",
       "      <td>  6</td>\n",
       "      <td> 11.3</td>\n",
       "      <td> 100.57</td>\n",
       "      <td>          Snow</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-12-31 15:00:00</th>\n",
       "      <td> -2.3</td>\n",
       "      <td> -4.6</td>\n",
       "      <td> 84</td>\n",
       "      <td>  9</td>\n",
       "      <td>  9.7</td>\n",
       "      <td> 100.47</td>\n",
       "      <td>          Snow</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-12-31 16:00:00</th>\n",
       "      <td> -1.4</td>\n",
       "      <td> -4.0</td>\n",
       "      <td> 82</td>\n",
       "      <td> 13</td>\n",
       "      <td> 12.9</td>\n",
       "      <td> 100.40</td>\n",
       "      <td>          Snow</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-12-31 17:00:00</th>\n",
       "      <td> -1.1</td>\n",
       "      <td> -3.3</td>\n",
       "      <td> 85</td>\n",
       "      <td> 19</td>\n",
       "      <td>  9.7</td>\n",
       "      <td> 100.30</td>\n",
       "      <td>          Snow</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-12-31 18:00:00</th>\n",
       "      <td> -1.3</td>\n",
       "      <td> -3.1</td>\n",
       "      <td> 88</td>\n",
       "      <td> 17</td>\n",
       "      <td>  9.7</td>\n",
       "      <td> 100.19</td>\n",
       "      <td>          Snow</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-12-31 19:00:00</th>\n",
       "      <td>  0.1</td>\n",
       "      <td> -2.7</td>\n",
       "      <td> 81</td>\n",
       "      <td> 30</td>\n",
       "      <td>  9.7</td>\n",
       "      <td> 100.13</td>\n",
       "      <td>          Snow</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-12-31 20:00:00</th>\n",
       "      <td>  0.2</td>\n",
       "      <td> -2.4</td>\n",
       "      <td> 83</td>\n",
       "      <td> 24</td>\n",
       "      <td>  9.7</td>\n",
       "      <td> 100.03</td>\n",
       "      <td>          Snow</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-12-31 21:00:00</th>\n",
       "      <td> -0.5</td>\n",
       "      <td> -1.5</td>\n",
       "      <td> 93</td>\n",
       "      <td> 28</td>\n",
       "      <td>  4.8</td>\n",
       "      <td>  99.95</td>\n",
       "      <td>          Snow</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-12-31 22:00:00</th>\n",
       "      <td> -0.2</td>\n",
       "      <td> -1.8</td>\n",
       "      <td> 89</td>\n",
       "      <td> 28</td>\n",
       "      <td>  9.7</td>\n",
       "      <td>  99.91</td>\n",
       "      <td>          Snow</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-12-31 23:00:00</th>\n",
       "      <td>  0.0</td>\n",
       "      <td> -2.1</td>\n",
       "      <td> 86</td>\n",
       "      <td> 30</td>\n",
       "      <td> 11.3</td>\n",
       "      <td>  99.89</td>\n",
       "      <td>          Snow</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8784 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                     Temp (C)  Dew Point Temp (C)  Rel Hum (%)  \\\n",
       "Date/Time                                                        \n",
       "2013-01-01 00:00:00      -1.0                -1.7           95   \n",
       "2013-01-01 01:00:00      -2.0                -5.1           79   \n",
       "2013-01-01 02:00:00      -2.7                -6.0           78   \n",
       "2013-01-01 03:00:00      -5.6               -11.7           62   \n",
       "2013-01-01 04:00:00      -7.7               -12.6           68   \n",
       "2013-01-01 05:00:00      -9.7               -14.8           66   \n",
       "2013-01-01 06:00:00     -11.1               -17.0           62   \n",
       "2013-01-01 07:00:00     -12.2               -17.2           66   \n",
       "2013-01-01 08:00:00     -13.0               -17.7           68   \n",
       "2013-01-01 09:00:00     -13.0               -17.3           70   \n",
       "2013-01-01 10:00:00     -12.6               -17.8           65   \n",
       "2013-01-01 11:00:00     -12.2               -17.6           64   \n",
       "2013-01-01 12:00:00     -11.8               -17.2           64   \n",
       "2013-01-01 13:00:00     -11.3               -17.4           61   \n",
       "2013-01-01 14:00:00     -11.3               -17.4           61   \n",
       "2013-01-01 15:00:00     -11.4               -17.6           60   \n",
       "2013-01-01 16:00:00     -12.0               -18.0           61   \n",
       "2013-01-01 17:00:00     -13.0               -18.4           64   \n",
       "2013-01-01 18:00:00     -13.4               -18.4           66   \n",
       "2013-01-01 19:00:00     -14.1               -18.7           68   \n",
       "2013-01-01 20:00:00     -14.3               -19.0           67   \n",
       "2013-01-01 21:00:00     -14.8               -19.5           67   \n",
       "2013-01-01 22:00:00     -16.3               -20.2           72   \n",
       "2013-01-01 23:00:00     -15.4               -19.8           69   \n",
       "2013-01-02 00:00:00     -14.0               -18.4           69   \n",
       "2013-01-02 01:00:00     -14.1               -18.3           70   \n",
       "2013-01-02 02:00:00     -14.3               -18.3           72   \n",
       "2013-01-02 03:00:00     -14.7               -18.0           76   \n",
       "2013-01-02 04:00:00     -14.2               -17.1           79   \n",
       "2013-01-02 05:00:00     -14.3               -17.0           80   \n",
       "...                       ...                 ...          ...   \n",
       "2012-12-30 18:00:00     -12.6               -16.0           76   \n",
       "2012-12-30 19:00:00     -13.4               -16.5           77   \n",
       "2012-12-30 20:00:00     -13.8               -16.5           80   \n",
       "2012-12-30 21:00:00     -13.8               -16.5           80   \n",
       "2012-12-30 22:00:00     -13.7               -16.3           81   \n",
       "2012-12-30 23:00:00     -12.1               -15.1           78   \n",
       "2012-12-31 00:00:00     -11.1               -14.4           77   \n",
       "2012-12-31 01:00:00     -10.7               -14.0           77   \n",
       "2012-12-31 02:00:00     -10.1               -13.4           77   \n",
       "2012-12-31 03:00:00     -11.8               -14.4           81   \n",
       "2012-12-31 04:00:00     -10.5               -12.8           83   \n",
       "2012-12-31 05:00:00     -10.2               -12.4           84   \n",
       "2012-12-31 06:00:00      -9.7               -11.7           85   \n",
       "2012-12-31 07:00:00      -9.3               -11.3           85   \n",
       "2012-12-31 08:00:00      -8.6               -10.3           87   \n",
       "2012-12-31 09:00:00      -8.1                -9.6           89   \n",
       "2012-12-31 10:00:00      -7.4                -8.9           89   \n",
       "2012-12-31 11:00:00      -6.7                -7.9           91   \n",
       "2012-12-31 12:00:00      -5.8                -7.5           88   \n",
       "2012-12-31 13:00:00      -4.6                -6.6           86   \n",
       "2012-12-31 14:00:00      -3.4                -5.7           84   \n",
       "2012-12-31 15:00:00      -2.3                -4.6           84   \n",
       "2012-12-31 16:00:00      -1.4                -4.0           82   \n",
       "2012-12-31 17:00:00      -1.1                -3.3           85   \n",
       "2012-12-31 18:00:00      -1.3                -3.1           88   \n",
       "2012-12-31 19:00:00       0.1                -2.7           81   \n",
       "2012-12-31 20:00:00       0.2                -2.4           83   \n",
       "2012-12-31 21:00:00      -0.5                -1.5           93   \n",
       "2012-12-31 22:00:00      -0.2                -1.8           89   \n",
       "2012-12-31 23:00:00       0.0                -2.1           86   \n",
       "\n",
       "                     Wind Spd (km/h)  Visibility (km)  Stn Press (kPa)  \\\n",
       "Date/Time                                                                \n",
       "2013-01-01 00:00:00               35              6.4            99.89   \n",
       "2013-01-01 01:00:00               35             16.1            99.93   \n",
       "2013-01-01 02:00:00               28             19.3           100.08   \n",
       "2013-01-01 03:00:00               30             25.0           100.21   \n",
       "2013-01-01 04:00:00               35             19.3           100.32   \n",
       "2013-01-01 05:00:00               33             25.0           100.47   \n",
       "2013-01-01 06:00:00               30             25.0           100.65   \n",
       "2013-01-01 07:00:00               20             25.0           100.78   \n",
       "2013-01-01 08:00:00               13             24.1           100.87   \n",
       "2013-01-01 09:00:00               20             24.1           100.86   \n",
       "2013-01-01 10:00:00               19             24.1           100.90   \n",
       "2013-01-01 11:00:00               22             24.1           100.88   \n",
       "2013-01-01 12:00:00               26             24.1           100.87   \n",
       "2013-01-01 13:00:00               26             24.1           100.83   \n",
       "2013-01-01 14:00:00               28             24.1           100.82   \n",
       "2013-01-01 15:00:00               30             24.1           100.85   \n",
       "2013-01-01 16:00:00               22             24.1           100.81   \n",
       "2013-01-01 17:00:00               19             25.0           100.90   \n",
       "2013-01-01 18:00:00               24             25.0           100.96   \n",
       "2013-01-01 19:00:00               20             25.0           101.02   \n",
       "2013-01-01 20:00:00               15             25.0           101.04   \n",
       "2013-01-01 21:00:00               15             25.0           100.98   \n",
       "2013-01-01 22:00:00                7             25.0           100.98   \n",
       "2013-01-01 23:00:00               11             25.0           100.99   \n",
       "2013-01-02 00:00:00               11             19.3           100.96   \n",
       "2013-01-02 01:00:00               11             25.0           100.91   \n",
       "2013-01-02 02:00:00               13             25.0           100.94   \n",
       "2013-01-02 03:00:00                9             19.3           100.91   \n",
       "2013-01-02 04:00:00                6              9.7           100.83   \n",
       "2013-01-02 05:00:00                0              6.4           100.81   \n",
       "...                              ...              ...              ...   \n",
       "2012-12-30 18:00:00               24             25.0           101.36   \n",
       "2012-12-30 19:00:00               26             25.0           101.47   \n",
       "2012-12-30 20:00:00               24             25.0           101.52   \n",
       "2012-12-30 21:00:00               20             25.0           101.50   \n",
       "2012-12-30 22:00:00               19             25.0           101.54   \n",
       "2012-12-30 23:00:00               28             25.0           101.52   \n",
       "2012-12-31 00:00:00               26             25.0           101.51   \n",
       "2012-12-31 01:00:00               15             25.0           101.50   \n",
       "2012-12-31 02:00:00                9             25.0           101.45   \n",
       "2012-12-31 03:00:00                6             25.0           101.42   \n",
       "2012-12-31 04:00:00               11             25.0           101.34   \n",
       "2012-12-31 05:00:00                6             25.0           101.28   \n",
       "2012-12-31 06:00:00                4             25.0           101.23   \n",
       "2012-12-31 07:00:00                0             19.3           101.19   \n",
       "2012-12-31 08:00:00                4              3.2           101.14   \n",
       "2012-12-31 09:00:00                4              2.4           101.09   \n",
       "2012-12-31 10:00:00                4              6.4           101.05   \n",
       "2012-12-31 11:00:00                9              9.7           100.93   \n",
       "2012-12-31 12:00:00                4             12.9           100.78   \n",
       "2012-12-31 13:00:00                4             12.9           100.63   \n",
       "2012-12-31 14:00:00                6             11.3           100.57   \n",
       "2012-12-31 15:00:00                9              9.7           100.47   \n",
       "2012-12-31 16:00:00               13             12.9           100.40   \n",
       "2012-12-31 17:00:00               19              9.7           100.30   \n",
       "2012-12-31 18:00:00               17              9.7           100.19   \n",
       "2012-12-31 19:00:00               30              9.7           100.13   \n",
       "2012-12-31 20:00:00               24              9.7           100.03   \n",
       "2012-12-31 21:00:00               28              4.8            99.95   \n",
       "2012-12-31 22:00:00               28              9.7            99.91   \n",
       "2012-12-31 23:00:00               30             11.3            99.89   \n",
       "\n",
       "                           Weather  \n",
       "Date/Time                           \n",
       "2013-01-01 00:00:00           Snow  \n",
       "2013-01-01 01:00:00   Mainly Clear  \n",
       "2013-01-01 02:00:00           Snow  \n",
       "2013-01-01 03:00:00          Clear  \n",
       "2013-01-01 04:00:00   Mainly Clear  \n",
       "2013-01-01 05:00:00          Clear  \n",
       "2013-01-01 06:00:00          Clear  \n",
       "2013-01-01 07:00:00          Clear  \n",
       "2013-01-01 08:00:00          Clear  \n",
       "2013-01-01 09:00:00          Clear  \n",
       "2013-01-01 10:00:00          Clear  \n",
       "2013-01-01 11:00:00   Mainly Clear  \n",
       "2013-01-01 12:00:00   Mainly Clear  \n",
       "2013-01-01 13:00:00   Mainly Clear  \n",
       "2013-01-01 14:00:00   Mainly Clear  \n",
       "2013-01-01 15:00:00   Mainly Clear  \n",
       "2013-01-01 16:00:00   Mainly Clear  \n",
       "2013-01-01 17:00:00          Clear  \n",
       "2013-01-01 18:00:00          Clear  \n",
       "2013-01-01 19:00:00          Clear  \n",
       "2013-01-01 20:00:00          Clear  \n",
       "2013-01-01 21:00:00   Mainly Clear  \n",
       "2013-01-01 22:00:00  Mostly Cloudy  \n",
       "2013-01-01 23:00:00         Cloudy  \n",
       "2013-01-02 00:00:00           Snow  \n",
       "2013-01-02 01:00:00  Mostly Cloudy  \n",
       "2013-01-02 02:00:00   Snow Showers  \n",
       "2013-01-02 03:00:00           Snow  \n",
       "2013-01-02 04:00:00           Snow  \n",
       "2013-01-02 05:00:00           Snow  \n",
       "...                            ...  \n",
       "2012-12-30 18:00:00   Mainly Clear  \n",
       "2012-12-30 19:00:00   Mainly Clear  \n",
       "2012-12-30 20:00:00          Clear  \n",
       "2012-12-30 21:00:00   Mainly Clear  \n",
       "2012-12-30 22:00:00   Mainly Clear  \n",
       "2012-12-30 23:00:00  Mostly Cloudy  \n",
       "2012-12-31 00:00:00         Cloudy  \n",
       "2012-12-31 01:00:00         Cloudy  \n",
       "2012-12-31 02:00:00         Cloudy  \n",
       "2012-12-31 03:00:00  Mostly Cloudy  \n",
       "2012-12-31 04:00:00         Cloudy  \n",
       "2012-12-31 05:00:00         Cloudy  \n",
       "2012-12-31 06:00:00         Cloudy  \n",
       "2012-12-31 07:00:00   Snow Showers  \n",
       "2012-12-31 08:00:00   Snow Showers  \n",
       "2012-12-31 09:00:00           Snow  \n",
       "2012-12-31 10:00:00       Snow,Fog  \n",
       "2012-12-31 11:00:00           Snow  \n",
       "2012-12-31 12:00:00           Snow  \n",
       "2012-12-31 13:00:00           Snow  \n",
       "2012-12-31 14:00:00           Snow  \n",
       "2012-12-31 15:00:00           Snow  \n",
       "2012-12-31 16:00:00           Snow  \n",
       "2012-12-31 17:00:00           Snow  \n",
       "2012-12-31 18:00:00           Snow  \n",
       "2012-12-31 19:00:00           Snow  \n",
       "2012-12-31 20:00:00           Snow  \n",
       "2012-12-31 21:00:00           Snow  \n",
       "2012-12-31 22:00:00           Snow  \n",
       "2012-12-31 23:00:00           Snow  \n",
       "\n",
       "[8784 rows x 7 columns]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "weather_2012 = pd.concat(data_by_month)\n",
    "weather_2012"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 5.4 Saving to a CSV"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It's slow and unnecessary to download the data every time, so let's save our dataframe for later use!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "weather_2012.to_csv('../data/weather_2012.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And we're done!"
   ]
  }
 ],
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